ZIP1+ fibroblasts protect lung cancer against chemotherapy via connexin-43 mediated intercellular Zn2+ transfer

Tumour–stroma cell interactions impact cancer progression and therapy responses. Intercellular communication between fibroblasts and cancer cells using various soluble mediators has often been reported. In this study, we find that a zinc-transporter (ZIP1) positive tumour-associated fibroblast subset is enriched after chemotherapy and directly interconnects lung cancer cells with gap junctions. Using single-cell RNA sequencing, we identify several fibroblast subpopulations, among which Zip1+ fibroblasts are highly enriched in mouse lung tumours after doxorubicin treatment. ZIP1 expression on fibroblasts enhances gap junction formation in cancer cells by upregulating connexin-43. Acting as a Zn2+ reservoir, ZIP1+ fibroblasts absorb and transfer Zn2+ to cancer cells, leading to ABCB1-mediated chemoresistance. Clinically, ZIP1high stromal fibroblasts are also associated with chemoresistance in human lung cancers. Taken together, our results reveal a mechanism by which fibroblasts interact directly with tumour cells via gap junctions and contribute to chemoresistance in lung cancer.

Editorial Note: This manuscript has been previously reviewed at another journal that is not operating a transparent peer review scheme. This document only contains reviewer comments and rebuttal letters for versions considered at Nature Communications.

REVIEWER COMMENTS
Reviewer #1 (Remarks to the Author): In the revised version the authors have responded to many of my comments and the manuscript is overall improved. However, the (very interesting) main claim is still not clearly supported by the data, and some of the data are still presented in a confusing manner. Several specific concerns also remain and need to be addressed before this manuscript can be accepted: 1. In response to my comments the authors tested whether chemotherapy induces the expression of Zip1 in CAFs, and the results demonstrated that this was not the case. In fact, the new data show that Dox treatment down regulated the expression of Zip1 (new western blot in Fig. 7, not quantified).
The authors now suggest that secreted S100A4 may be responsible for Zip1 upregulation (rather than a direct effect of chemotherapy). This may be the case. However, they state in the revised text that "After screening, we found that S100A4.." What screening was performed? Why was S100A4 a candidate? This needs to be clearly explained.
The authors now show in vitro that exogenous S100A4 (not explained in the text) can induce Zip1 expression in fibroblasts. But what is the cellular source of S100A4 in vivo? Based on new Figure 7j, cancer cells upregulate secretion of S100A4 following chemotherapy. To mechanistically tie up these new observations, the authors should show that CM from Dox-treated tumor cells can upregulate Zip1 in fibroblasts, and that targeting of S100A4 (e.g with siRNA) inhibits this upregulation.
2. Fig S1a: what are the arrows pointing at? Not clear and not explained in legends. 3. In response to my comment that western blots should be quantified, the authors responded that "For limited space, we did not include the semi-quantification results of western blots". This is not an acceptable response. Quantifications can be included in supplementary information if space is limiting, but they should be performed. This is true also for the new western blots in Fig. 7. 4. Figure 2i: what is the arrow in the right panel pointing at? There is no explanation in the legends. 5. In response to my previous comment #14 regarding Fig. 4e, the authors responded: "We are sorry for the confusion. We have reviewed the origin Fig. 4e to clearly show a visible difference". I am not sure what the authors mean by "reviewed the origin". However, in the figure it still seems that TPEN does not inhibit the expression of cx43, whereas HEPT does. This too would be less confusing if WB were quantified. 6. Although the authors responded to my request for English editing by stating that they have done so, the manuscript is still poorly written and needs to be corrected for English grammar in some parts. For example (out of multiple others): We propose that ZIP1+S100A4+CX43high CAF may be called as zinc412 transport CAF (zCAF) that absorbs and transfers Zn2+ to neighboring cancer cells through gap junctions. Except for zCAF, our results indicate that ZIP1 might also (be) expressed in fibroblasts.." Reviewer #2 (Remarks to the Author): Ni et al. revised most of concerns raised by this reviewer, although some of important questions remind unclear and they left them as speculative thoughts in discussion. Nevertheless, they indeed identified a unique population of CAF expressing ZIP1, that may directly control cancer cells to become chemoresistance by zinc influx through functional gap junctions which upregulates ABCB1 for drug extrusion. They have successfully revised and modify the previous version of manuscript which sounds improved, so that from this reviewer there is no further requests and comments for publication.
Reviewer #3 (Remarks to the Author): The authors have responded appropriately to all of the reviewers' comments including providing additional data. The manuscript and study would have been significantly improved if they had other preclinical models besides the LLC model-and there are several such cell line syngeneic models available that are derived from genetic engineered mouse models. They have increased their data on human patient tumor responses and that is particularly important.
Reviewer #4 (Remarks to the Author): new reviewer with expertise in CAFs, scRNAseq In this manuscript, Ni et al., describe a unique Zip1+ CAF population with the ability to communicate with tumor cells via connexin-43, enabling Zn2+ transfer and facilitating chemoresistance. They conducted single cell analyses of stromal populations derived from subcutaneous LLC tumors treated with Dox and demonstrate the enrichment of Zip1+ fibroblasts in response to treatment. They supplement their in-silico findings with experimental substantiation and show the relevance of Zip1 expressing fibroblasts in determining clinical outcomes. This is an important and relatively underinvestigated are of research where the authors have done a commendable assessment of interactions between CAFs and cancer cells. My main concerns relate to the analysis of single cell data, annotation of CAF subtypes and relevance to human single cell RNA seq data in lung cancer. Please see major and minor comments detailed below: Major comments: 1. Based on the clustering resolution it seems that C1 CAFs in Figure 1c most likely represent an activation program rather than a distinct CAF cluster (clustering resolutions are subjective and in this case the boundaries seem to be arbitrary based on the choice of the resolution parameter). Can you comment on which genes are expressed in this cluster (disregarding their specificity)? Are they shared with Zip1+ CAFs? In such cases it is useful to use topic modeling approaches to show changes in activation programs ( 3. It is unclear how these CAF subsets relate to previously described Il1 CAFs, TGF-beta driven CAFs etc. It is important to show which signaling pathways are activated in these subsets. Please use Progeny to determine signaling pathway responsive genes. It will be interesting to see if the Zip1+ fibroblasts are driven by a completely different pathway.

Reviewer #1 (Remarks to the Author):
In the revised version the authors have responded to many of my comments and the manuscript is overall improved. However, the (very interesting) main claim is still not clearly supported by the data, and some of the data are still presented in a confusing manner. Several specific concerns also remain and need to be addressed before this manuscript can be accepted: 1. In response to my comments the authors tested whether chemotherapy induces the expression of Zip1 in CAFs, and the results demonstrated that this was not the case. In fact, the new data show that Dox treatment down regulated the expression of Zip1 (new western blot in Fig. 7, not quantified). The authors now suggest that secreted S100A4 may be responsible for Zip1 upregulation (rather than a direct effect of chemotherapy). This may be the case. However, they state in the revised text that "After screening, we found that S100A4.." What screening was performed? Why was S100A4 a candidate? This needs to be clearly explained. Figure  7j, cancer cells upregulate secretion of S100A4 following chemotherapy. To mechanistically tie up these new observations, the authors should show that CM from Dox-treated tumor cells can upregulate Zip1 in fibroblasts, and that targeting of S100A4 (e.g with siRNA) inhibits this upregulation. Response: Thanks for the reviewer's insightful comments and suggestions. Indead, we have examined the effects of several secretary factors including TGFβ, TNF, IFN and S100A4 on ZIP1 expression in fibroblasts. Only S100A4 could upregulate ZIP1 expression in fibroblasts. For clarity and focus reason, we did not display the results for TGFβ, TNF and IFN and deleted "After screening". We have added "Other's and our own previous studies demonstrated that S100A4 often increases in tissues under stress 1, 2, 3, 4 " in the revised manuscript to explain why we chose S100A4 as a candidate (Page 15 line 313-314).

The authors now show in vitro that exogenous S100A4 (not explained in the text) can induce Zip1 expression in fibroblasts. But what is the cellular source of S100A4 in vivo? Based on new
As the reviewer suggested, we performed experiments to examine whether DOX treatment increases the release of S100A4 by tumor cells, and whether S100A4 in culture medium from DOX-treated tumor cells can upregulate ZIP1 in fibroblasts. The results showed that DOX treatment increased the release of S100A4 into the supernatants of tumor cells (Fig. 7c, Fig. S7c). Compared to the control medium, ZIP1 expression in fibroblasts was upregulated by the culture medium from DOXtreated tumor cells, which could be reversed by anti-S100A4 antibody 3B11 (Fig. 7d, Fig. S7d) (Page 15 line 314-317).
As the reviewer suggested, we have quantified western-blotting results in Fig. 7. 2. Fig S1a: what are the arrows pointing at? Not clear and not explained in legends. Response: We apologize for the confusion. The arrows mean DOX injection at day 0, 2 and 4. We have explained the arrows in the legend of Fig S1a (Supplementary figures and figure legends). 2 Fig. 7. Response: Thanks for the reviewer's kind suggestions. As the reviewer suggested, we have quantified the western-blotting results including Fig. 7 in our revised manuscript, and the results are included in supplementary information. Figure 2i: what is the arrow in the right panel pointing at? There is no explanation in the legends. Response: We apologize for the confusion. The arrow points at the position of hCAF in the graph. We have explained the arrow in the legend of  Fig. 4e, we adjusted the brightness and contrast of the raw picture to reveal group difference. As the reviewer suggested, we have quantified western blot in Fig. 4e (Fig. S4f).

4.
6. Although the authors responded to my request for English editing by stating that they have done so, the manuscript is still poorly written and needs to be corrected for English grammar in some parts. For example (out of multiple others): We propose that ZIP1+S100A4+CX43high CAF may be called as zinc412 transport CAF (zCAF) that absorbs and transfers Zn2+ to neighboring cancer cells through gap junctions. Except for zCAF, our results indicate that ZIP1 might also (be) expressed in fibroblasts." Response: Thanks for the reviewer's kind suggestions. We have asked a professional language editing company (Editage, www.editage.cn) to improve the English writing of our manuscript again. Hopefully, current version of the manuscript is error free and suitable for publication.

Ni et al. revised most of concerns raised by this reviewer, although some of important questions remind unclear and they left them as speculative thoughts in discussion. Nevertheless, they indeed identified a unique population of CAF expressing ZIP1, that may directly control cancer cells to become chemoresistance by zinc influx through functional gap junctions which upregulates ABCB1 for drug extrusion. They have successfully revised and modify the previous version of manuscript which sounds improved, so that from this reviewer there is no further requests and comments for publication.
Response: We deeply appreciate the reviewer's kind comments. 3

The authors have responded appropriately to all of the reviewers' comments including providing additional data. The manuscript and study would have been significantly improved if they had other preclinical models besides the LLC model-and there are several such cell line syngeneic models available that are derived from genetic engineered mouse models. They have increased their data on human patient tumor responses and that is particularly important.
Response: Thanks for the reviewer's kind comments. We will consider examining more preclinical lung cancer models in our future studies.

Reviewer #4 (Remarks to the Author): new reviewer with expertise in CAFs, scRNAseq In this manuscript, Ni et al., describe a unique Zip1+ CAF population with the ability to communicate with tumor cells via connexin-43, enabling Zn2+ transfer and facilitating chemoresistance. They conducted single cell analyses of stromal populations derived from subcutaneous LLC tumors treated with Dox and demonstrate the enrichment of Zip1+ fibroblasts in response to treatment. They supplement their in-silico findings with experimental substantiation and show the relevance of Zip1 expressing fibroblasts in determining clinical outcomes. This is an important and relatively under-investigated are of research where the authors have done a commendable assessment of interactions between CAFs and cancer cells. My main concerns relate to the analysis of single cell data, annotation of CAF subtypes and relevance to human single cell RNA seq data in lung cancer. Please see major and minor comments detailed below:
Major comments: 1. Based on the clustering resolution it seems that C1 CAFs in Figure 1c most likely represent an activation program rather than a distinct CAF cluster (clustering resolutions are subjective and in this case the boundaries seem to be arbitrary based on the choice of the resolution parameter). Can you comment on which genes are expressed in this cluster (disregarding their specificity)? Are they shared with Zip1+ CAFs? In such cases it is useful to use topic modeling approaches to show changes in activation programs (refer cNMF Kotliar et al., eLife 2019) Response: Thanks for the reviewer's insightful comments. As the reviewer suggested, we performed cNMF analysis of C0-C3. We identified the top 100 genes for each GEP (Table S3) and performed a GO (Gene Ontology) enrichment analysis to explore the function of each GEP (Table  S4). Relating to the clusters and programs, we found that of the four clusters, cluster 0 was strongly enriched for developmental GEP1, cluster 1 was strongly enriched for metabolic GEP2, cluster 2 was strongly enriched for matrix GEP3, and cluster 3 was strongly enriched for proliferating GEP4, further supporting that they are distinct clusters (Fig. S1i). Although there were no specific markers, cluster 1 highly expressed metabolism-related genes, such as Gchfr, Mif, and Cox8a, that were shared with other clusters (Fig. S1j). (Results, page 6-7 line 125-133. Methods, page 29 line 626-630).

Communications 2018 (breast cancer), Zilionis et al., Immunity 2019 (lung cancer)? It would be important to map the Zip1+ fibroblast signature derived from your dataset to these single cell datasets to determine whether these CAF subsets truly exist in multiple indications.
Response: Thanks for the reviewer's kind comments. We agree that it is important to mount bioinformatic and experimental evidences in support of the ubiquity of ZIP1+ fibroblasts across cancer types. However, whether ZIP1+ fibroblasts play similar functions in different cancer types is unclear, and needs careful study individually. Therefore, in this study, we majorly focus our study of ZIP1+ fibroblasts in lung cancer. We have mapped the ZIP1+ fibroblast signature to CAF subtypes in the human NSCLC data (Fig. S8, Table S6-7, page 15-16 line 330-346). We found that there is a corresponding ZIP1+ fibroblast subset in the human NSCLC (Fig. S8). The immunostaining of ZIP1+ fibroblasts in transplanted Pan02 tumor further supports their existence in mouse tumor model, and is a good implication for further study of ZIP1+ fibroblasts in pancreatic cancer as well as other cancer types. As the reviewer suggested, we will consider the research of ZIP1+ fibroblast function in other tumors in the future.

It is unclear how these CAF subsets relate to previously described Il1 CAFs, TGF-beta driven
CAFs etc. It is important to show which signaling pathways are activated in these subsets. Please use Progeny to determine signaling pathway responsive genes. It will be interesting to see if the Zip1+ fibroblasts are driven by a completely different pathway. Response: Thanks for the reviewer's insightful comments and suggestions. As the reviewer suggested, we have performed PROGENy to determine signaling pathway responsive genes in each cluster (Methods, page 29 line 632-635). The PI3K signalling pathway was active in cluster 0 Zip1 + CAFs, while the TGFβ signalling pathway was active in cluster 6 and 7 myofibroblasts, the JAK-STAT signalling pathway was active in cluster 4 fibrocytes, and the VEGF signalling pathway was active in cluster 2 (Fig. S1k). It has been reported that TGFβ stimulates myofibroblast phenotype and IL-1 (activating JAK-STAT) promotes inflammatory fibroblasts 5, 6 . Therefore, cluster 0 Zip1 + CAFs might be driven by a distinct PI3K signalling pathway compared to other clusters. (Page 7 line 133-140).

RNA velocity results suggest that these Zip1+ CAFs are able to give rise to cluster 2 CAFs -how does this compare to previous observations where Dpt+ universal fibroblasts give rise to activated fibroblasts such as Lrrc15+ CAFs in different cancer indications (Buechler & Pradhan 2021). Do Zip1+ fibroblasts express other markers of stemness that would pinpoint to this particular function?
It is important to mention here which genes are upregulated in the transition from Zip1+/ Cluster 0 to cluster 2 and Cluster A0 to A01. Response: Thanks for the reviewer's insightful comments. Cluster 0 Zip1 + CAFs moderately expressed Dpt and cluster 2 CAFs expressed higher Dpt (Fig. S1h), indicating these fibroblasts are similar to Dpt+ universal fibroblasts. Cluster 0 Zip1 + CAFs expressed Notch2, which has been reported to be critical for the maintenance of cell stemness in hematopoietic cells and neural stem cells 7,8 . (Page 6 line 123-125). As the reviewer suggested, we evaluated gene upregulation during cluster transition. For example, Plod2, Nnmt and Col3a1 were upregulated in transition from Cluster 0 to Cluster 2, while Spry2, Mt2 and Mt1 were upregulated in transition within cluster 0 ( Fig. S1lo, Table S5). (Results, page 7 line 145-147, Methods, page 29 line 621-624). 5 Maynard et al., Cell 2020, the authors study therapy induced adaptation in advanced NSCLC using single cell RNA seq. They show that cancer cells surviving on therapy progressive disease upregulate gap-junction pathways. As such it would be important to check whether Zip1+ fibroblast signature from this manuscript's dataset maps to CAF subtypes in the human NSCLC data. Response: Thanks for the reviewer's insightful comments and suggestions. We have discussed the study of Maynard et al. in Discussion. We added "In support of our results, Maynard et al. reported that cancer cells surviving on therapy progressive disease upregulated the gap junction pathway in lung cancer patients with targeted therapy 9 " in the revised manuscript. (Page 20 line 423-425).

In
We agree to the reviewer that it is important to check whether Zip1+ fibroblast signature maps to CAF subtypes in the human NSCLC data. Indeed, we have performed these analyses in our manuscript (Fig. S8, Table S6-7, page 15-16 line 330-346). We found that there is a corresponding ZIP1+ fibroblast subset in the human NSCLC.

For Cluster 4 expressing both macrophage and fibroblast markers, can you check if this is truly
the case or these represent doublets? If so, please use scrublet to remove these cells. Response: Thanks for the reviewer's comments and suggestions. We performed Scrublet to check doublets in our dataset. We identified overall 5.2% doublets in total cells and especially 7.6% in Cluster 4 ( Figure R1a). We compared the expression of feature genes of Cluster 4 (Itgm, CD14, Lyz2, Dcn, Fn1, Sparce, Spp1) in original dataset and dataset removed doublets. The results showed that the expression pattern of feature genes (Itgm, CD14, Lyz2, Dcn, Fn1, Sparce, Spp1) in the two datasets was almost the same (Figure R1b), suggesting that the expression of both macrophage and fibroblast markers by cluster 4 was not caused by doublets. A recent study also identified fibroblasts expressed both fibroblast and macrophage markers and proposed macrophage-myofibroblast transition mechanism for generation of these cells 10 Figure 1f, S1e: Please change to a faceted bar graph for PBS versus Dox treated samples for ease in interpreting the visualization. Ensure that colors for clusters in the bar graph match the UMAP colors in Figure 1c. Response: Thanks for the reviewer's kind suggestions. As the reviewer suggested, we have changed Figure 1f, S1e to a faceted bar graph.

2.
3. Figure 1h: Please denote the RNA velocity direction clearly in the figure panel as it is challenging to see the direction of the arrow. Adding cluster labels will help with clarity in visualization. Response: Thanks for the reviewer's kind suggestions. As the reviewer suggested, we have added cluster labels and denoted the RNA velocity direction in the figure panel (Figure 1h).

REVIEWERS' COMMENTS
Reviewer #1 (Remarks to the Author): In this revised version, the authors have addressed my comments and the manuscript is much improved. One last comment remain before the paper can be accepted: In new Fig. 7C, there is no loading control (all other blots have b-actin). This should be included. In fact, how was the gel quantified without loading control? In principle, quantification should be relative to controls, but also to loading control. How quantifications were performed is not explained in legends or figures and it should be clearly stated.
Reviewer #4 (Remarks to the Author): Ni et al., have adequately addressed my concerns regarding the bioinformatics analysis and interpretation of the data. While it is not entirely clear whether this CAF can be annotated as a new CAF type, experimental findings do suggest that Zip1+ CAFs emerge uniquely in response to chemotherapy. In light of improved analyses and successful modification of the manuscript, there are no further comments for publication.